Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Artificial intelligence (AI) technologies, such as natural language processing (NLP), have been around for some time, and more recently there has been much hype surrounded the potential of combining AI with Machine Learning (ML) for decision making. But has it met the challenge? This webinar reviews what NLP is, the role NLP plays in machine learning approaches, such as deep learning, and some real-world use cases for application to life sciences and healthcare to improve patient outcomes.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
It seems that AI is also becoming a buzzword, like design thinking. Everyone is talking about AI or wants to have AI, and sees all the ideas and benefits – that’s fine, but how do you get started? But what’s different now? Three innovations have finally put AI on the fast track: Big Data, with the internet and sensors everywhere; massive computing power, especially through the Cloud; and the development of breakthrough algorithms, so computers can be trained to accomplish more sophisticated tasks on their own with deep learning. If you use new technology, you need to explore and know what’s possible. With design thinking, it aids to outline the steps and define the ways in which you’re going to create the solution. Starting with mapping the customer journey, defining who will be using that service enhanced with intelligent technology, or who will benefit and gain value from it. We discuss how these two worlds are coming together, and how you get started to transform your venture with Artificial Intelligence using Design Thinking.
Speaker: Claudio Mirti, Principal Solution Specialist – Data & AI, Microsoft
The webinar explores some of the current opportunities for AI within Life Science and look ahead to what we can expect to see over the coming years. These are the accompanying slides.
Pistoia Alliance Webinar Demystifying AI: Centre of Excellence for AI Webina...Pistoia Alliance
Pistoia Alliance launched its Centre of Excellence for Artificial Intelligence (AI) in Life Sciences where we hope to bring together best practice, adoption strategy and hackathons covering a range of challenges.
Over the coming months we will be hosting a series of topics and speakers giving their perspectives on the role of Artificial & Augmented Intelligence in Life Sciences and Healthcare.
The topics will cover some of the current challenges, user stories & value in using AI in life sciences. If you want to get involved in this series as a speaker or suggest topics please get in touch
Webinar 1 will focused on the following
A Brief History
Big Data/ML/DL/AI - fundamentals and concepts
Data Fidelity importance
Some best practices
Artificial intelligence (AI) technologies, such as natural language processing (NLP), have been around for some time, and more recently there has been much hype surrounded the potential of combining AI with Machine Learning (ML) for decision making. But has it met the challenge? This webinar reviews what NLP is, the role NLP plays in machine learning approaches, such as deep learning, and some real-world use cases for application to life sciences and healthcare to improve patient outcomes.
AI-SDV 2021: Francisco Webber - Efficiency is the New PrecisionDr. Haxel Consult
The global data sphere, consisting of machine data and human data, is growing exponentially reaching the order of zettabytes. In comparison, the processing power of computers has been stagnating for many years. Artificial Intelligence – a newer variant of Machine Learning – bypasses the need to understand a system when modelling it; however, this convenience comes with extremely high energy consumption.
The complexity of language makes statistical Natural Language Understanding (NLU) models particularly energy hungry. Since most of the zettabyte data sphere consists of human data, such as texts or social networks, we face four major obstacles:
1. Findability of Information – when truth is hard to find, fake news rule
2. Von Neumann Gap – when processors cannot process faster, then we need more of them (energy)
3. Stuck in the Average – when statistical models generate a bias toward the majority, innovation has a hard time
4. Privacy – if user profiles are created “passively” on the server side instead of “actively” on the client side, we lose control
The current approach to overcoming these limitations is to use larger and larger data sets on more and more processing nodes for training. AI algorithms should be optimized for efficiency rather than precision. In this case, statistical modelling should be disqualified as a brute force approach for language applications. When replacing statistical modelling and arithmetic, set theory and geometry seem to be a much better choice as it allows the direct processing of words instead of their occurrence counts, which is exactly what the human brain does with language – using only 7 Watts!
It seems that AI is also becoming a buzzword, like design thinking. Everyone is talking about AI or wants to have AI, and sees all the ideas and benefits – that’s fine, but how do you get started? But what’s different now? Three innovations have finally put AI on the fast track: Big Data, with the internet and sensors everywhere; massive computing power, especially through the Cloud; and the development of breakthrough algorithms, so computers can be trained to accomplish more sophisticated tasks on their own with deep learning. If you use new technology, you need to explore and know what’s possible. With design thinking, it aids to outline the steps and define the ways in which you’re going to create the solution. Starting with mapping the customer journey, defining who will be using that service enhanced with intelligent technology, or who will benefit and gain value from it. We discuss how these two worlds are coming together, and how you get started to transform your venture with Artificial Intelligence using Design Thinking.
Speaker: Claudio Mirti, Principal Solution Specialist – Data & AI, Microsoft
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
NDC Oslo : A Practical Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
(1) I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
(2) Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
(3) The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
A conference report of SemTechBiz 2013 in San Francisco, from a datamining and knowledge-management point of view. It covers several companies with their automatic algorithms to extract data from cleverly discovered crowed-curated data sources, or using UI tools to leverage existing utility to lure user help mark up the data...
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
Declarative Multilingual Information Extraction with SystemTdiannepatricia
"Declarative Multilingual Information Extraction with SystemT" presented by Laura Chiticariu, IBM Research - Almaden as part of the Cognitive Systems Institute Speaker Series.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
NDC Oslo : A Practical Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
(1) I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
(2) Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
(3) The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
A conference report of SemTechBiz 2013 in San Francisco, from a datamining and knowledge-management point of view. It covers several companies with their automatic algorithms to extract data from cleverly discovered crowed-curated data sources, or using UI tools to leverage existing utility to lure user help mark up the data...
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
Declarative Multilingual Information Extraction with SystemTdiannepatricia
"Declarative Multilingual Information Extraction with SystemT" presented by Laura Chiticariu, IBM Research - Almaden as part of the Cognitive Systems Institute Speaker Series.
An Ontology for Learning Services on the Shop FloorCarsten Ullrich
An ontology expresses a common understanding of a domain that serves as a basis of communication between people or systems, and enables knowledge sharing, reuse of domain knowledge, reasoning and thus problem solving. In Technology-Enhanced Learning, especially in Intelligent Tutoring Systems and Adaptive Learning Environments, ontologies serve as the basis of adaptivity and personalization. For mathematics learning and similarly structured domains, ontologies and their usage for adaptive learning are well understood and established. This contribution presents an ontology for the industrial shop floor (the area of a factory where operatives assemble products) and illustrates its usage in several learning services.
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
Big Data Expo 2015 - Cisco Connected AnalyticsBigDataExpo
The presentation will describe the Internet of Everything technology transition, where people, process, data and things are coming together to unleash 14,4 Trillion dollars global economic value.
The question is how do we capture this value by connecting the unconnected, while carving out actionable, replicable insights from Big Data ? The speech will include practical cases on how enterprises – including Cisco – and public sector agencies are able today to unleash economic, social and environmental value through data-intensive, new IT consumption models
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Phoenix Data Conference - Big Data Analytics for IoT 11/4/17Mark Goldstein
“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.
As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.
Putting the L in front: from Open Data to Linked Open DataMartin Kaltenböck
Keynote presentation of Martin Kaltenböck (LOD2 project, Semantic Web Company) at the Government Linked Data Workshop in the course of the OGD Camp 2011 in Warsaw, Poland: Putting the L in front: from Open Data to Linked Open Data
Building Reference Architectures for the Industrial IoTCapgemini
Building Reference Architectures for the Industrial IoT - Alina Chircu, Bentley University; Eldar Sultanow, Capgemini Germany
Workshop on Smart Manufacturing in Aerospace and Automotive Industries
AIS Special Interest Group for Big Data Application in Processes (SIGBD), AMCIS 2017, Boston, USA
Software Architecture and the role of the Architect has been discussed and deliberated in detail. Architecture still plays major role in success of projects. While the fundamentals remain strong, how architects can contribute in teams success while in agile is an ongoing journey. As the team member endowed with skills and wisdom acquired over the experience frame, we argue Architects are best positioned to prepare a road-map of architectural aspects and participate in planning together with product owners and release owners thus enabling a more meaningful planning and guidance system. Based on Risk and Cost Based Architecture concept by Eltjo Poort and based on CAFFEA framework by Jan Bosch and team, we applied it in projects which led to seeing it as a six stepped approach described in the slides.
A Framework for Geospatial Web Services for Public Health by Dr. Leslie LenertWansoo Im
A Framework for Geospatial Web Services for Public Health
by Leslie Lenert, MD, MS, FACMI, Director
National Center for Public Health Informatics, CCHIS, CDC
June 8 2009 URISA Public Health Conference
uploaded by Wansoo Im, Ph.D.
URISA Membership Committee Chair
http://www.gisinpublichealth.org
These slides were used at the first Aarhus Follower Group meet-up for the EU-funded project IoTCrawler. They entail an introduction to the project aswell as a more in depth presentation of the difference between web search and Internet of Things (IoT) search an the development of Internet of Things. Furthermore some of the scenarios from the project are presented.
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Teaching cognitive computing with ibm watsondiannepatricia
Ralph Badinelli, Lenz Chair in the Department of Business Information Technology, Pamplin College of Business of Virginia Tech. presented "Teaching Cognitive Computing with IBM Watson" as part of the Cognitive Systems Institute Speaker Series.
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
José Hernández-Orallo, Full Professor, Department of Information Systems and Computation at the Universitat Politecnica de València, presentation “Evaluating Cognitive Systems: Task-oriented or Ability-oriented?” as part of the Cognitive Systems Institute Speaker Series.
Building Compassionate Conversational Systemsdiannepatricia
Rama Akkiraju, Distinguished Engineer and Master Inventor at IBM, presention "Building Compassionate Conversational Systems" as part of the Cognitive Systems Institute Speaker Series.
“Artificial Intelligence, Cognitive Computing and Innovating in Practice”diannepatricia
Cristina Mele, Full Professor of Management at the University of Napoli “Federico II”, presentation as part of Cognitive Systems Institute Speaker Series
Eric Manser and Will Scott from IBM Research, presentation on "Cognitive Insights Drive Self-driving Accessibility" as part of the Cognitive Systems Institute Speaker Series
Roberto Sicconi and Malgorzata (Maggie) Stys, founders of TeleLingo, presented "AI in the Car" as part of the Cognitive Systems Institute Speaker Series.
“Semantic PDF Processing & Document Representation”diannepatricia
Sridhar Iyengar, IBM Distinguished Engineer at the IBM T. J. Watson Research Center, presention “Semantic PDF Processing & Document Representation” as part of the Cognitive Systems Institute Group Speaker Series.
Joining Industry and Students for Cognitive Solutions at Karlsruhe Services R...diannepatricia
Gerhard Satzger, Director of the Karlsruhe Service Research Institute and two former students and IBMers, Sebastian Hirschl and Kathrin Fitzer, presention"Joining Industry and Students for Cognitive Solutions at Karlsruhe Services Research Center" as part of the Cognitive Systems Institute Speaker Series.
170330 cognitive systems institute speaker series mark sherman - watson pr...diannepatricia
Dr. Mark Sherman, Director of the Cyber Security Foundations group at CERT within CMU’s Software Engineering Institute. , presention “Experiences Developing an IBM Watson Cognitive Processing Application to Support Q&A of Application Security Diagnostics” as part of the Cognitive Systems Institute Speaker Series.
“Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption”diannepatricia
Chuck Howell, Chief Engineer for Intelligence Programs and Integration at the MITRE Corporation, presentation “Fairness Cases as an Accelerant and Enabler for Cognitive Assistance Adoption” as part of the Cognitive Systems Institute Speaker Series.
From complex Systems to Networks: Discovering and Modeling the Correct Network"diannepatricia
From complex Systems to Networks: Discovering and Modeling the Correct Network" by Nitesh Chawla as part of the Cognitive Systems Institute Speaker Series
Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and director of the research center on network and data sciences (iCeNSA) at the University of Notre Dame.
Developing Cognitive Systems to Support Team Cognitiondiannepatricia
Steve Fiore from the University of Central Florida presented “Developing Cognitive Systems to Support Team Cognition” as part of the Cognitive Systems Institute Speaker Series
Kevin Sullivan from the University of Virginia presented: "Cyber-Social Learning Systems: Take-Aways from First Community Computing Consortium Workshop on Cyber-Social Learning Systems" as part of the Cognitive Systems Institute Speaker Series.
“IT Technology Trends in 2017… and Beyond”diannepatricia
William Chamberlin, IBM Distinguished Market Intelligence Professional, presented “IT Technology Trends in 2017… and Beyond” as part of the Cognitive Systems Institute Speaker Series on January 26, 2017.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
1. KIT – The Research University in the Helmholtz Association www.kit.edu
Institute of Applied Informatics and Formal Description Methods (AIFB), Karlsruhe Service Research Institute (KSRI),
FZI Research Center for Information Technology
Semantic Technologies for Smart Services
Rudi Studer & Maria Maleshkova
Cognitive Systems Institute Speaker Series, 15 December 2016
2. Institute of Applied Informatics and Formal
Description Methods (AIFB)
2
“Semantic Karlsruhe”
Industrie 4.0
Medicine &
eHealth
Digital Shift
Big Data &
Data Analytics
SEMANTIC TECHNOLOGIES
Semantic
Data Management
Complex Event
Processing
Data / Text Mining
Smart
Services
Basic
Research
Applied
Research
Transfer
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
3. Institute of Applied Informatics and Formal
Description Methods (AIFB)
3
WEB SCIENCE AND
KNOWLEDGE MANAGEMENT
Institute of Applied
Informatics and Formal
Description Methods
4. Institute of Applied Informatics and Formal
Description Methods (AIFB)
4
Karlsruhe Service Research Institute – an „industry-on-
campus“ model with focus on interdisciplinary research
Prof. Dr. Christof
Weinhardt
Information & Market
Engineering
Prof. Dr. Gerhard
Satzger
Digital Service
Innovation
Prof. Dr. Stefan Nickel
Discrete Optimization
& Logistics
Prof. Dr. Wolf Fichtner
Energy Economics
Prof. Dr. Alexander
Mädche
Information Systems &
Service Design
Prof. Dr. Rudi Studer
Knowledge Management
Prof. Dr. York Sure-
Vetter
Prof. Dr. Kai Furmans
Value Stream Services
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Dr. Markus Bauer
5. Institute of Applied Informatics and Formal
Description Methods (AIFB)
5
Service Research investigates complex service systems where
economic value is created jointly by multiple independent parties, acting
together efficiently through the systematic use of information and
communication technologies…
…from different perspectives and in different domains
... and others
Healthcare
Services
Crowd and
Participation
Services
(e)-Mobility
Smart
Services,
Industry 4.0
and IoT
Research Focus:
Intelligent Services for Real-world Networks
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
6. Institute of Applied Informatics and Formal
Description Methods (AIFB)
6
• Motivation
• Why Smart Services via Semantic Technologies?
• Use Case 1 - Building Agile Systems
• Use Case 2 – Smart Services for Predictive
Maintenance
• Summary and Conclusions
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
7. Institute of Applied Informatics and Formal
Description Methods (AIFB)
7
Market
Influence
Technology
Development
Today’s Driving Forces
Shorter innovation cycles
Need for continuous adaptation
Near real-time analyses
Involvement of the customer not only with
the finished product/service but during the
complete development cycle
Ubiquitous access
Social and community Web
Heterogeneous big data
Distributed component-based
solutions
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
8. Institute of Applied Informatics and Formal
Description Methods (AIFB)
8
Internet of Things (IoT) Challenges
We expect one hundred billion IoT devices to be deployed
within the next ten years
BUT the IoT is currently facing a lot of problems
Product silos that do not interoperate with each other
Many approaches and incompatible platforms
No network effect
Heterogeneity in terms of
Data
Devices and interfaces
Data volumes and number of sources explode
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
We expect one hundred billion IoT devices to be deployed
within the next ten years
BUT the IoT is currently facing a lot of problems
Product silos that do not interoperate with each other
Many approaches and incompatible platforms
No network effect
Heterogeneity in terms of
Data
Devices and interfaces
Data volumes and number of sources explode
see: http://www.w3.org/2015/05/wot-framework.pdf
9. Institute of Applied Informatics and Formal
Description Methods (AIFB)
9
The Web as the Solution
Source: http://www.w3.org/2015/05/wot-framework.pdf
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
10. Institute of Applied Informatics and Formal
Description Methods (AIFB)
10
Semantic Technologies for Smart Services
Data Integration – combining data from multiple sources enables
new applications and insights
More and more data available on the Web is published conforming to
Semantic Web standards
Linking Open Data (LOD) initiative
Semantic Web technologies are beneficial for data exchange, integration
and search
Decentralised Architectures – no central controller or repository
Overcoming device heterogeneity – common model for devices
(functional and non-functional properties)
Overcoming interface heterogeneity – standard Web Technologies +
Linked Data
Adaptation – adjusting services, products, things according to context
and current needs
Intelligent Programmable Interfaces
Embedding intelligence into the service interface (e.g. rules)
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
11. Institute of Applied Informatics and Formal
Description Methods (AIFB)
11
Semantic Technologies
Semantic Web technologies,
standardised by the W3C, are
mature:
RDF recommendation in 1999,
update in 2004
RDFa (RDF in HTML) note in 2008
RDFS recommendation in 2004
SPARQL recommendation in 2008
OWL recommendation in 2004,
update in 2009
Linked Data is a subset of the
Semantic Web stack
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
12. Institute of Applied Informatics and Formal
Description Methods (AIFB)
12
Use Cases
1. Building Agile Systems
Fast integration of data and programmable interfaces based on semantic
technologies
2. Smart Services for Predictive Maintenance
Semantics for integrating sensor data, background knowledge and
decision rules
Recognizing maintenance
needs before they occur
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Semantic
description
Semantic
description
Semantic
description
13. Institute of Applied Informatics and Formal
Description Methods (AIFB)
13
BUILDING AGILE SYSTEMS
Semantics for integrating data and programmable interfaces
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
14. Institute of Applied Informatics and Formal
Description Methods (AIFB)
14
Today‘s Web is about Dynamic Data
Data is often dynamically created as a result of some calculation
carried out over input data (e.g., weather information)
Data can change frequently (e.g., moving objects)
APIs are used to trigger functionalities in the Web and the real world
and provide access to dynamic and static data sources
An important role plays
Representational State Transfer
(REST)
Architectural style for client–
server interaction
Compatible with Web architecture
http://programmableweb.com
8816 APIs
Over 16,400 APIs and 7,800 mashups
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
15. Institute of Applied Informatics and Formal
Description Methods (AIFB)
15
Linked Data Principles
1. Use URIs to name things; not only documents, but also people, locations,
concepts, etc.
http://dbpedia.org/resource/Johannes_Gutenberg
2. To enable agents (human users and machine agents alike) to look up those
names, use HTTP URIs
http://dbpedia.org/page/Printing_press
3. When someone looks up a URI we provide useful information; with 'useful' in
the strict sense we usually mean structured data in RDF
http://dbpedia.org/page/Printing_press
dct:subject dbc:Johannes_Gutenberg.
4. Include links to other URIs allowing agents (machines and humans) to
discover more things
<http://dbpedia.org/page/Printing_press> rdfs:seeAlso
<http://dbpedia.org/page/Letterpress_printing> .
http://www.w3.org/DesignIssues/LinkedData
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
16. Institute of Applied Informatics and Formal
Description Methods (AIFB)
16
Linking Open Data Cloud
Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
17. Institute of Applied Informatics and Formal
Description Methods (AIFB)
17
Motivation for Combining Semantics and
Services
Increased value comes from combinations of services and
APIs
But a lot of manual effort is required for this compositions (glue code)
Structured service/API descriptions ease the composition process considerably
Semantic descriptions allow for execution of several tasks automatically
(e.g., data matching, discovery, ranking)
Manually drafted
glue code
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Semantic
description
Semantic
description
Semantic
description
18. Institute of Applied Informatics and Formal
Description Methods (AIFB)
18
Motivation for Combining Semantics and
Services
Increased value comes from combinations of services and
APIs
But a lot of manual effort is required for this compositions (glue code)
Structured service/API descriptions ease the composition process considerably
Semantic descriptions allow for execution of several tasks automatically
(e.g., data matching, discovery, ranking)
Manually drafted
glue code
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Semantic
description
Semantic
description
Semantic
description
19. Institute of Applied Informatics and Formal
Description Methods (AIFB)
19
Creating Linked Services
Functionality attainable via the Web by combining:
RESTful services (respecting Web architecture)
resource-oriented
manipulated with HTTP verbs
GET, PUT (, PATCH), POST, DELETE
Negotiate representations
Linked data
Uniform use of URIs
Use of RDF and SPARQL
= Linked Services
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
20. Institute of Applied Informatics and Formal
Description Methods (AIFB)
20
Facilitate Data Integration
Linked Service
Combines data (MashUp)
build on top
Application
that consumes one
Linked Service
Bad solution
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
21. Institute of Applied Informatics and Formal
Description Methods (AIFB)
21
Facilitate Data Integration
Linked Service
Combines data (MashUp)
build on top
Application
that consumes one
Linked Service
Bad solution
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
22. Institute of Applied Informatics and Formal
Description Methods (AIFB)
22
Facilitate Data Integration
Linked
Service
Application
(integrates data and
functionalities from several
Linked Services, e.g. via Linked
Data-Fu)
Good solution
Linked
Service
Linked
Service
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
http://linked-data-fu.github.io/
23. Institute of Applied Informatics and Formal
Description Methods (AIFB)
23
SMART SERVICES FOR
PREDICTIVE MAINTENANCE
Semantics for integrating sensor data, background knowledge and
decision rules
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
24. Institute of Applied Informatics and Formal
Description Methods (AIFB)
24
Cognition Framework
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Perception Reaction
Background
Knowledge
Interpretation and
Analysis
25. Institute of Applied Informatics and Formal
Description Methods (AIFB)
25
The Cognition Framework for Predictive
Maintenance
Input data in terms of
- Sensor data
- Personal observations
- Alarms and errors
Background knowledge
- Log files
- Previous similar problems
and solutions
- Guidelines
- Manuals
- Detail about the machines
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Interpretation and Analysis
- Data integration to enable
analysis
- Similarity analysis with
previous problems
- Heuristics encoded as rules
Reaction
- Automated solution
recommendation vs.
- Providing solution support
26. Institute of Applied Informatics and Formal
Description Methods (AIFB)
26
Problem Breakdown
1. Smart Services for Problem Recognition
Recognizing what the current problem is based on previous problems
Combination with heuristics
2. Smart Services for preparing Solution Containers
Providing summary of the problem, difficulty, time estimate
Links to relevant manuals, links to required parts
Required expertise, contacts of people with relevant qualifications
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
?
http://www.aifb.kit.edu/web/STEP/en
27. Institute of Applied Informatics and Formal
Description Methods (AIFB)
27
Problem Breakdown
2. Smart Services for preparing Solution Containers (continued)
Dealing with multilingual and multimodal sources
Identifying related articles across different languages and media types
Possible use – the solution might be available in another language;
images and videos can be used to identify the problem, support the solution
3. Smart Services for Interactive
Problem Solving
Guiding the user towards the solution
Recommending the next possible step
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
http://xlime.eu/
28. Institute of Applied Informatics and Formal
Description Methods (AIFB)
28
Problem Breakdown
4. Smart Services for Route Planning for the technician
Supporting the dispatcher in planning the routes
Supporting the technician during the trips
Solution based on Use Case 1: Building Agile Systems
Creating Linked Services for the interfaces
Rules for defining the composition and interaction
Automated execution with Linked DataFu
Prototype system for data / service integration and execution
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
Proximity service
Street View
Maintenance route-planning
29. Institute of Applied Informatics and Formal
Description Methods (AIFB)
29
Summary and Outlook
Market trends and technology developments pave the way for
developing new products and services, which are more flexible and
adapted to the customer needs
We need technology solutions to achieve more automation and
adaptability –– putting the ‘Smartness’ into services
Providing means for agile system development
Providing means for self-adaptivity
We can use Semantic Technologies for Smart Services to support:
The rapid development of mashups and applications
To realize Industry 4.0 / IoT solutions
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series
30. Institute of Applied Informatics and Formal
Description Methods (AIFB)
30
Relevant Publications
S. Stadtmüller, S. Speiser, A. Harth, R. Studer
Data-Fu: A Language and an Interpreter for Interaction with Read/Write Linked Data.
Proceedings of the 22nd International Conference on World Wide Web, pp. 1225-1236, Rio
de Janeiro, 2013.
A. Harth, C. Knoblock, S. Stadtmüller, R. Studer, und P. Szekely. On-the-fly Integration of
Static and Dynamic Sources. Proceedings of the ISWC Workshop
on Consuming Linked Data. 2013: CEUR-WS.
M. Maleshkova, P. Philipp, Y. Sure-Vetter, R. Studer. Smart Web Services (SmartWS) –
The Future of Services on the Web. IPSI BgD Transactions on Advanced Research
(TAR), 12 (1), pp. 15-26, January, 2016.
T. Weller, M. Maleshkova, K. März, L. Maier-Hein. A RESTful Approach for Developing
Medical Decision Support Systems. The Semantic Web: ESWC 2015 Satellite
Events, pp. 376-384, Springer, 9341.
T. Weller, M. Maleshkova. Cognitive Process - An Open-Source Tool to Capture
Processes according to the Linked Data Principles. The Semantic Web: ESWC 2016
Satellite Events, Springer.
L. Zhang, A. Rettinger, J. Zhang. A Knowledge Base Approach to Cross-Lingual
Keyword Query Interpretation. The 15th International Semantic Web Conference
(ISWC'16), Springer, Oktober, 2016
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series